Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (1): 97-104.doi: 10.6040/j.issn.1672-3961.0.2025.190

• Civil Engineering • Previous Articles    

Question-answering model for building material carbon emissions unit conversion based on retrieval-augmented generation and Agent

YAN Qiao1,2, JIAO Fei3, YAN Yi1,2*, DU Xianghua4, LIU Pengcheng1   

  1. YAN Qiao1, 2, JIAO Fei3, YAN Yi1, 2*, DU Xianghua4, LIU Pengcheng1(1. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China;
    2. Shandong Key Laboratory of Smart Buildings and Energy Efficiency, Shandong Jianzhu University, Jinan 250101, Shandong, China;
    3. Heze Branch of China Tower Co., Ltd., Heze 274000, Shandong, China;
    4. School of Computer and Artificial Intelligence, Shandong Jianzhu University, Jinan 250101, Shandong, China
  • Published:2026-02-03

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Abstract: To solve mismatching between the measurement units of building materials and the units of carbon emission factors during the calculation of carbon emissions in the phase of building materials production and transportation, a question-answering model for building material carbon emissions unit conversion based on retrieval-augmented generation(RAG)and Agent was proposed. A local knowledge database was constructed by analyzing typical material conversion processes, and a RAG module was designed to provide step-by-step references for the conversions. An Agent capable of calling the calculation tool was developed to perform the mathematical operations required in the conversion process. The prompt templates were designed and integrated with a large language model to answer the question based on local knowledge database. The experimental results showed that the proposed model could accurately answer the unit conversion questions of building materials, and realized visualization of the unit conversion results and reasoning steps displayed on the Web interface and local console.

Key words: building material carbon emissions, unit conversion, retrieval-augmented generation, Agent, question-answering model

CLC Number: 

  • TU5
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